Abstract
Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X) systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on: i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles.
Highlights
Localization is one of the main pillars of Intelligent Transportation Systems (ITS)
The rest of the paper is organized as follows: Section II presents the graph regulizer used for exploiting spatial coherences; Section III presents the batch sequential solution that exploits the low-dimensional subspace of the high-dimensional location data; Section IV is dedicated to the experimental setup and simulation results while Section V concludes our work
Kinematic models of vehicles and data extracted by open source autonomous driving simulators (e.g. CARLA) exhibit low-rank properties that are attributed to the fact that for various time periods clusters of vehicles move in parallel directions
Summary
Localization is one of the main pillars of Intelligent Transportation Systems (ITS). Global Navigation Satellite System (GNSS), e.g GPS, provide absolute position information, their accuracy is limited and deviations up to 10 m or higher may arise [8], [10], especially in harsh environments such as urban canyons and tunnels. CL is based on the 5G communication technology V2X, allowing the vehicles of a Vehicular-Ad-hoc-NETwork (VANET) to share information among them, in order to improve the position accuracy. The VANET of [4], fuses absolute position and range measurements, using Extended Kalman Filter (EKF) and CIF, in a Distributed manner. In [7], a Centralized CL method based on Generalized Approximate Message Passing and Kalman Filter (KF) is developed It exploits navigation measurements from Inertial Navigation Unit, GPS, signals of opportunity, ground stations and inter-vehicular measurements. In [9], a Distributed Bayesian approach that fuses GPS and inter-vehicle distance measurements, is employed in order to perform CL. Highdimensional location trajectories often lie in a low-dimensional subspace and can be recovered more accurately when using exact low-rank matrix recovery approaches methods We exploit this property of motion patterns using low-rank modelling and optimization tools. The rest of the paper is organized as follows: Section II presents the graph regulizer used for exploiting spatial coherences; Section III presents the batch sequential solution that exploits the low-dimensional subspace of the high-dimensional location data; Section IV is dedicated to the experimental setup and simulation results while Section V concludes our work
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